
"""simple docstring"""






import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu



class 			lowercase					( unittest.TestCase		):






			def 					UpperCAmelCase							(self  :	Any			)       ->	Tuple:





				"""simple docstring"""


				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()






			@property
			def 					UpperCAmelCase							(self  :	Optional[Any]			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  1
				lowerCAmelCase												=  3
				lowerCAmelCase												=  (32, 32)

				lowerCAmelCase												=  floats_tensor((batch_size, num_channels) + sizes      ,rng=random.Random(0			)			).to(SCREAMING_SNAKE_CASE_			)
				return image






			@property
			def 					UpperCAmelCase							(self  :	Union[str, Any]			)       ->	int:





				"""simple docstring"""


				torch.manual_seed(0			)
				lowerCAmelCase												=  UNetaDConditionModel(
				    block_out_channels=(32, 64)      ,layers_per_block=2      ,sample_size=32      ,in_channels=4      ,out_channels=4      ,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')      ,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')      ,cross_attention_dim=32      ,)
				return model






			@property
			def 					UpperCAmelCase							(self  :	Dict			)       ->	Any:





				"""simple docstring"""


				torch.manual_seed(0			)
				lowerCAmelCase												=  AutoencoderKL(
				    block_out_channels=[32, 64]      ,in_channels=3      ,out_channels=3      ,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']      ,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']      ,latent_channels=4      ,)
				return model






			@property
			def 					UpperCAmelCase							(self  :	Tuple			)       ->	str:





				"""simple docstring"""


				torch.manual_seed(0			)
				lowerCAmelCase												=  CLIPTextConfig(
				    bos_token_id=0      ,eos_token_id=2      ,hidden_size=32      ,intermediate_size=37      ,layer_norm_eps=1e-05      ,num_attention_heads=4      ,num_hidden_layers=5      ,pad_token_id=1      ,vocab_size=1_000      ,)
				return CLIPTextModel(SCREAMING_SNAKE_CASE_			)






			@property
			def 					UpperCAmelCase							(self  :	str			)       ->	int:





				"""simple docstring"""


				def extract(*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	str			):



					class 			lowercase					:






								def __init__(self  :	List[Any]			)       ->	Dict:





									"""simple docstring"""


									lowerCAmelCase												=  torch.ones([0]			)






								def 					UpperCAmelCase							(self  :	int      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]			)       ->	List[str]:





									"""simple docstring"""


									self.pixel_values.to(SCREAMING_SNAKE_CASE_			)
									return self





					return Out()

				return extract






			def 					UpperCAmelCase							(self  :	Optional[Any]			)       ->	Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase												=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
				lowerCAmelCase												=  self.dummy_cond_unet
				lowerCAmelCase												=  DDIMScheduler(
				    beta_start=0.0_00_85      ,beta_end=0.0_12      ,beta_schedule='''scaled_linear'''      ,clip_sample=SCREAMING_SNAKE_CASE_      ,set_alpha_to_one=SCREAMING_SNAKE_CASE_      ,)

				lowerCAmelCase												=  self.dummy_vae
				lowerCAmelCase												=  self.dummy_text_encoder
				lowerCAmelCase												=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

				# make sure here that pndm scheduler skips prk
				lowerCAmelCase												=  StableDiffusionPipeline(
				    unet=SCREAMING_SNAKE_CASE_      ,scheduler=SCREAMING_SNAKE_CASE_      ,vae=SCREAMING_SNAKE_CASE_      ,text_encoder=SCREAMING_SNAKE_CASE_      ,tokenizer=SCREAMING_SNAKE_CASE_      ,safety_checker=SCREAMING_SNAKE_CASE_      ,feature_extractor=self.dummy_extractor      ,)
				lowerCAmelCase												=  sd_pipe.to(SCREAMING_SNAKE_CASE_			)
				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  '''A painting of a squirrel eating a burger'''

				lowerCAmelCase												=  torch.Generator(device=SCREAMING_SNAKE_CASE_			).manual_seed(0			)
				lowerCAmelCase												=  sd_pipe([prompt]      ,generator=SCREAMING_SNAKE_CASE_      ,guidance_scale=6.0      ,num_inference_steps=2      ,output_type='''np'''			)
				lowerCAmelCase												=  output.images

				lowerCAmelCase												=  torch.Generator(device=SCREAMING_SNAKE_CASE_			).manual_seed(0			)
				lowerCAmelCase												=  sd_pipe(
				    [prompt]      ,generator=SCREAMING_SNAKE_CASE_      ,guidance_scale=6.0      ,num_inference_steps=2      ,output_type='''np'''      ,return_dict=SCREAMING_SNAKE_CASE_      ,)[0]

				lowerCAmelCase												=  image[0, -3:, -3:, -1]
				lowerCAmelCase												=  image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)
				lowerCAmelCase												=  np.array([0.57_56, 0.61_18, 0.50_05, 0.50_41, 0.54_71, 0.47_26, 0.49_76, 0.48_65, 0.48_64]			)

				assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2






			def 					UpperCAmelCase							(self  :	Dict			)       ->	Optional[int]:





				"""simple docstring"""


				lowerCAmelCase												=  '''cpu'''  # ensure determinism for the device-dependent torch.Generator
				lowerCAmelCase												=  self.dummy_cond_unet
				lowerCAmelCase												=  PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  self.dummy_vae
				lowerCAmelCase												=  self.dummy_text_encoder
				lowerCAmelCase												=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

				# make sure here that pndm scheduler skips prk
				lowerCAmelCase												=  StableDiffusionPipeline(
				    unet=SCREAMING_SNAKE_CASE_      ,scheduler=SCREAMING_SNAKE_CASE_      ,vae=SCREAMING_SNAKE_CASE_      ,text_encoder=SCREAMING_SNAKE_CASE_      ,tokenizer=SCREAMING_SNAKE_CASE_      ,safety_checker=SCREAMING_SNAKE_CASE_      ,feature_extractor=self.dummy_extractor      ,)
				lowerCAmelCase												=  sd_pipe.to(SCREAMING_SNAKE_CASE_			)
				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  '''A painting of a squirrel eating a burger'''
				lowerCAmelCase												=  torch.Generator(device=SCREAMING_SNAKE_CASE_			).manual_seed(0			)
				lowerCAmelCase												=  sd_pipe([prompt]      ,generator=SCREAMING_SNAKE_CASE_      ,guidance_scale=6.0      ,num_inference_steps=2      ,output_type='''np'''			)

				lowerCAmelCase												=  output.images

				lowerCAmelCase												=  torch.Generator(device=SCREAMING_SNAKE_CASE_			).manual_seed(0			)
				lowerCAmelCase												=  sd_pipe(
				    [prompt]      ,generator=SCREAMING_SNAKE_CASE_      ,guidance_scale=6.0      ,num_inference_steps=2      ,output_type='''np'''      ,return_dict=SCREAMING_SNAKE_CASE_      ,)[0]

				lowerCAmelCase												=  image[0, -3:, -3:, -1]
				lowerCAmelCase												=  image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)
				lowerCAmelCase												=  np.array([0.51_25, 0.57_16, 0.48_28, 0.50_60, 0.56_50, 0.47_68, 0.51_85, 0.48_95, 0.49_93]			)

				assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2






			def 					UpperCAmelCase							(self  :	Dict			)       ->	Union[str, Any]:





				"""simple docstring"""


				lowerCAmelCase												=  StableDiffusionPipeline.from_pretrained(
				    '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''      ,safety_checker=SCREAMING_SNAKE_CASE_			)
				assert isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)
				assert isinstance(pipe.scheduler      ,SCREAMING_SNAKE_CASE_			)
				assert pipe.safety_checker is None

				lowerCAmelCase												=  pipe('''example prompt'''      ,num_inference_steps=2			).images[0]
				assert image is not None

				# check that there's no error when saving a pipeline with one of the models being None
				with tempfile.TemporaryDirectory() as tmpdirname:
					pipe.save_pretrained(SCREAMING_SNAKE_CASE_			)
					lowerCAmelCase												=  StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_			)

				# sanity check that the pipeline still works
				assert pipe.safety_checker is None
				lowerCAmelCase												=  pipe('''example prompt'''      ,num_inference_steps=2			).images[0]
				assert image is not None






			@unittest.skipIf(torch_device != '''cuda'''      ,'''This test requires a GPU'''			)
			def 					UpperCAmelCase							(self  :	Dict			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  self.dummy_cond_unet
				lowerCAmelCase												=  PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  self.dummy_vae
				lowerCAmelCase												=  self.dummy_text_encoder
				lowerCAmelCase												=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

				# put models in fp16
				lowerCAmelCase												=  unet.half()
				lowerCAmelCase												=  vae.half()
				lowerCAmelCase												=  bert.half()

				# make sure here that pndm scheduler skips prk
				lowerCAmelCase												=  StableDiffusionPipeline(
				    unet=SCREAMING_SNAKE_CASE_      ,scheduler=SCREAMING_SNAKE_CASE_      ,vae=SCREAMING_SNAKE_CASE_      ,text_encoder=SCREAMING_SNAKE_CASE_      ,tokenizer=SCREAMING_SNAKE_CASE_      ,safety_checker=SCREAMING_SNAKE_CASE_      ,feature_extractor=self.dummy_extractor      ,)
				lowerCAmelCase												=  sd_pipe.to(SCREAMING_SNAKE_CASE_			)
				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  '''A painting of a squirrel eating a burger'''
				lowerCAmelCase												=  sd_pipe([prompt]      ,num_inference_steps=2      ,output_type='''np'''			).images

				assert image.shape == (1, 64, 64, 3)





@nightly
@require_torch_gpu
class 			lowercase					( unittest.TestCase		):






			def 					UpperCAmelCase							(self  :	str			)       ->	int:





				"""simple docstring"""


				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	Dict:





				"""simple docstring"""


				lowerCAmelCase												=  StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''      ,safety_checker=SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config			)
				lowerCAmelCase												=  sd_pipe.to(SCREAMING_SNAKE_CASE_			)
				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  (
				    '''portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'''
				    ''' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'''
				    ''' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'''
				    ''' children from bahnhof zoo, detailed '''
				)
				lowerCAmelCase												=  4_003_660_346
				lowerCAmelCase												=  7

				# without safety guidance (sld_guidance_scale = 0)
				lowerCAmelCase												=  torch.manual_seed(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  sd_pipe(
				    [prompt]      ,generator=SCREAMING_SNAKE_CASE_      ,guidance_scale=SCREAMING_SNAKE_CASE_      ,num_inference_steps=50      ,output_type='''np'''      ,width=512      ,height=512      ,sld_guidance_scale=0      ,)

				lowerCAmelCase												=  output.images
				lowerCAmelCase												=  image[0, -3:, -3:, -1]
				lowerCAmelCase												=  [0.22_78, 0.22_31, 0.22_49, 0.23_33, 0.23_03, 0.18_85, 0.22_73, 0.21_44, 0.21_76]

				assert image.shape == (1, 512, 512, 3)

				assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2

				# without safety guidance (strong configuration)
				lowerCAmelCase												=  torch.manual_seed(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  sd_pipe(
				    [prompt]      ,generator=SCREAMING_SNAKE_CASE_      ,guidance_scale=SCREAMING_SNAKE_CASE_      ,num_inference_steps=50      ,output_type='''np'''      ,width=512      ,height=512      ,sld_guidance_scale=2_000      ,sld_warmup_steps=7      ,sld_threshold=0.0_25      ,sld_momentum_scale=0.5      ,sld_mom_beta=0.7      ,)

				lowerCAmelCase												=  output.images
				lowerCAmelCase												=  image[0, -3:, -3:, -1]
				lowerCAmelCase												=  [0.23_83, 0.22_76, 0.2_36, 0.21_92, 0.21_86, 0.20_53, 0.19_71, 0.19_01, 0.17_19]

				assert image.shape == (1, 512, 512, 3)

				assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2






			def 					UpperCAmelCase							(self  :	Tuple			)       ->	List[str]:





				"""simple docstring"""


				lowerCAmelCase												=  StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''      ,safety_checker=SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config			)
				lowerCAmelCase												=  sd_pipe.to(SCREAMING_SNAKE_CASE_			)
				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  '''padme amidala taking a bath artwork, safe for work, no nudity'''
				lowerCAmelCase												=  2_734_971_755
				lowerCAmelCase												=  7

				lowerCAmelCase												=  torch.manual_seed(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  sd_pipe(
				    [prompt]      ,generator=SCREAMING_SNAKE_CASE_      ,guidance_scale=SCREAMING_SNAKE_CASE_      ,num_inference_steps=50      ,output_type='''np'''      ,width=512      ,height=512      ,sld_guidance_scale=0      ,)

				lowerCAmelCase												=  output.images
				lowerCAmelCase												=  image[0, -3:, -3:, -1]
				lowerCAmelCase												=  [0.35_02, 0.36_22, 0.33_96, 0.36_42, 0.34_78, 0.33_18, 0.35, 0.33_48, 0.32_97]

				assert image.shape == (1, 512, 512, 3)

				assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2

				lowerCAmelCase												=  torch.manual_seed(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  sd_pipe(
				    [prompt]      ,generator=SCREAMING_SNAKE_CASE_      ,guidance_scale=SCREAMING_SNAKE_CASE_      ,num_inference_steps=50      ,output_type='''np'''      ,width=512      ,height=512      ,sld_guidance_scale=2_000      ,sld_warmup_steps=7      ,sld_threshold=0.0_25      ,sld_momentum_scale=0.5      ,sld_mom_beta=0.7      ,)

				lowerCAmelCase												=  output.images
				lowerCAmelCase												=  image[0, -3:, -3:, -1]
				lowerCAmelCase												=  [0.55_31, 0.52_06, 0.48_95, 0.51_56, 0.51_82, 0.47_51, 0.48_02, 0.48_03, 0.44_43]

				assert image.shape == (1, 512, 512, 3)

				assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2






			def 					UpperCAmelCase							(self  :	List[str]			)       ->	Optional[int]:





				"""simple docstring"""


				lowerCAmelCase												=  StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''			)
				lowerCAmelCase												=  sd_pipe.to(SCREAMING_SNAKE_CASE_			)
				sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

				lowerCAmelCase												=  (
				    '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
				    ''' leyendecker'''
				)
				lowerCAmelCase												=  1_044_355_234
				lowerCAmelCase												=  12

				lowerCAmelCase												=  torch.manual_seed(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  sd_pipe(
				    [prompt]      ,generator=SCREAMING_SNAKE_CASE_      ,guidance_scale=SCREAMING_SNAKE_CASE_      ,num_inference_steps=50      ,output_type='''np'''      ,width=512      ,height=512      ,sld_guidance_scale=0      ,)

				lowerCAmelCase												=  output.images
				lowerCAmelCase												=  image[0, -3:, -3:, -1]
				lowerCAmelCase												=  np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]			)

				assert image.shape == (1, 512, 512, 3)

				assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-7

				lowerCAmelCase												=  torch.manual_seed(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  sd_pipe(
				    [prompt]      ,generator=SCREAMING_SNAKE_CASE_      ,guidance_scale=SCREAMING_SNAKE_CASE_      ,num_inference_steps=50      ,output_type='''np'''      ,width=512      ,height=512      ,sld_guidance_scale=2_000      ,sld_warmup_steps=7      ,sld_threshold=0.0_25      ,sld_momentum_scale=0.5      ,sld_mom_beta=0.7      ,)

				lowerCAmelCase												=  output.images
				lowerCAmelCase												=  image[0, -3:, -3:, -1]
				lowerCAmelCase												=  np.array([0.58_18, 0.62_85, 0.68_35, 0.60_19, 0.6_25, 0.67_54, 0.60_96, 0.63_34, 0.65_61]			)
				assert image.shape == (1, 512, 512, 3)

				assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2




"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


UpperCAmelCase        =				logging.get_logger(__name__)



class 			lowercase					( lowercase__		):






			def __init__(self  :	str      ,*SCREAMING_SNAKE_CASE_  :	Any      ,**SCREAMING_SNAKE_CASE_  :	Optional[int]			)       ->	None:





				"""simple docstring"""


				warnings.warn(
				    '''The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
				    ''' Please use PerceiverImageProcessor instead.'''      ,SCREAMING_SNAKE_CASE_      ,)
				super().__init__(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)


